




"""simple docstring"""

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 	UpperCamelCase__  (		lowercase__	:  Any ):
		return ConvertCommand(
		    args.model_type							,  args.tf_checkpoint							,  args.pytorch_dump_output							,  args.config							,  args.finetuning_task_name )


__A					=      "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"
class       lowerCamelCase__     (	__lowerCAmelCase      ):


			@staticmethod
			def 				lowerCamelCase_					(  SCREAMING_SNAKE_CASE				):



					"""simple docstring"""







					snake_case		:    Any             =					parser.add_parser(
					    "convert"					,			help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."					,			)
					train_parser.add_argument("--model_type"					,			type=lowerCamelCase_					,			required=lowerCamelCase_					,			help="Model's type."				)
					train_parser.add_argument(
					    "--tf_checkpoint"					,			type=lowerCamelCase_					,			required=lowerCamelCase_					,			help="TensorFlow checkpoint path or folder."				)
					train_parser.add_argument(
					    "--pytorch_dump_output"					,			type=lowerCamelCase_					,			required=lowerCamelCase_					,			help="Path to the PyTorch saved model output."				)
					train_parser.add_argument("--config"					,			type=lowerCamelCase_					,			default=""					,			help="Configuration file path or folder."				)
					train_parser.add_argument(
					    "--finetuning_task_name"					,			type=lowerCamelCase_					,			default=lowerCamelCase_					,			help="Optional fine-tuning task name if the TF model was a finetuned model."					,			)
					train_parser.set_defaults(func=lowerCamelCase_				)


			def __init__(  self					,			SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE					,			SCREAMING_SNAKE_CASE					,			*SCREAMING_SNAKE_CASE					,			):



					"""simple docstring"""







					snake_case		:    List[str]             =					logging.get_logger("transformers-cli/converting"				)

					self._logger.info(F'''Loading model {model_type}'''				)
					snake_case		:    Tuple             =					model_type
					snake_case		:    Dict             =					tf_checkpoint
					snake_case		:    Tuple             =					pytorch_dump_output
					snake_case		:    Union[str, Any]             =					config
					snake_case		:    List[str]             =					finetuning_task_name



			def 				lowerCamelCase_					(  self				):



					"""simple docstring"""







					if self._model_type == "albert":
							try:
									from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
									    convert_tf_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(lowerCamelCase_				)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output				)
					elif self._model_type == "bert":
							try:
									from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
									    convert_tf_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(lowerCamelCase_				)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output				)
					elif self._model_type == "funnel":
							try:
									from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
									    convert_tf_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(lowerCamelCase_				)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output				)
					elif self._model_type == "t5":
							try:
									from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
									raise ImportError(lowerCamelCase_				)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output				)
					elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output				)
					elif self._model_type == "transfo_xl":
							try:
									from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
									    convert_transfo_xl_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(lowerCamelCase_				)

							if "ckpt" in self._tf_checkpoint.lower():
									snake_case		:    List[str]             =					self._tf_checkpoint
									snake_case		:    List[Any]             =					""
							else:
									snake_case		:    str             =					self._tf_checkpoint
									snake_case		:    Any             =					""
							convert_transfo_xl_checkpoint_to_pytorch(
							    lowerCamelCase_					,			self._config					,			self._pytorch_dump_output					,			lowerCamelCase_				)
					elif self._model_type == "gpt2":
							try:
									from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
									    convert_gpta_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(lowerCamelCase_				)

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output				)
					elif self._model_type == "xlnet":
							try:
									from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
									    convert_xlnet_checkpoint_to_pytorch,
									)
							except ImportError:
									raise ImportError(lowerCamelCase_				)

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output					,			self._finetuning_task_name				)
					elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint					,			self._pytorch_dump_output				)
					elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint					,			self._pytorch_dump_output				)
					elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint					,			self._config					,			self._pytorch_dump_output				)
					else:
							raise ValueError(
							    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"				)


_SCREAMING_SNAKE_CASE 		=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def 			lowercase(	)		->	None:







 '''simple docstring'''
 UpperCamelCase			  =      input("""Enter message: """       )
 UpperCamelCase			  =      input("""Enter key [alphanumeric]: """       )
 UpperCamelCase			  =      input("""Encrypt/Decrypt [e/d]: """       )

 if mode.lower().startswith("""e"""       ):
  UpperCamelCase			  =      """encrypt"""
  UpperCamelCase			  =      encrypt_message(UpperCamelCase_							,					UpperCamelCase_       )
 elif mode.lower().startswith("""d"""       ):
  UpperCamelCase			  =      """decrypt"""
  UpperCamelCase			  =      decrypt_message(UpperCamelCase_							,					UpperCamelCase_       )

 print(f"""\n{mode.title()}ed message:"""       )
 print(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""encrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""decrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      []
 UpperCamelCase			  =      0
 UpperCamelCase			  =      key.upper()

 for symbol in message:
  UpperCamelCase			  =      LETTERS.find(symbol.upper()       )
  if num != -1:
   if mode == "encrypt":
    num += LETTERS.find(key[key_index]       )
   elif mode == "decrypt":
    num -= LETTERS.find(key[key_index]       )

   num %= len(UpperCamelCase_       )

   if symbol.isupper():
    translated.append(LETTERS[num]       )
   elif symbol.islower():
    translated.append(LETTERS[num].lower()       )

   key_index += 1
   if key_index == len(UpperCamelCase_       ):
    UpperCamelCase			  =      0
  else:
   translated.append(UpperCamelCase_       )
 return "".join(UpperCamelCase_       )


if __name__ == "__main__":
     main()
